A Review of Optimal Demand Response Planning for Residential Households through Aggregators
Subject Areas : Electrical engineering (electronics, telecommunications, power, control)Gholam-Reza Kamyab 1 , Mohammad Hossein Erfani Majd 2 , سعید بلوچیان 3
1 - Department of Electrical Engineering, Gonabad Branch, Islamic Azadi University
2 - Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
3 - گروه مهندسی برق، واحد گناباد، دانشگاه آزاد اسلامی، گناباد، ایران
Keywords: Demand response, Aggregator, Smart grid, Energy consumption management,
Abstract :
This paper presents a comprehensive review of optimization strategies for residential demand response through aggregators. As the pressure on power networks increases significantly due to rising demand, effective planning and implementation of demand response strategies have become critically important. Key approaches discussed in the reviewed literature include the use of nonlinear and intelligent optimization methods, economic modeling, statistical analyses, and data mining processes, along with a focus on environmental aspects and energy efficiency improvements. The notable benefits of utilizing aggregators include reduced energy costs, enhanced grid stability, increased flexibility, and a significant decrease in emissions. However, challenges such as technical complexities, the need for effective coordination among various stakeholders, and infrastructural limitations exist in implementing these strategies. To overcome these challenges, the paper recommends employing advanced data analytics, developing novel and innovative aggregation models, and implementing effective energy management strategies. The review findings indicate that the appropriate and effective utilization of aggregators can achieve key optimization goals for demand response in residential sectors, which not only enhances the efficiency of power networks but also significantly helps in reducing consumer energy costs. Given the diversity and breadth of existing methods, further research and development in this area are encouraged, particularly regarding the integration of new technologies and the enhancement of active consumer participation in effective demand response programs.
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[1] Fei Wang, Biao Xiang, Kangping Li, Xinxin Ge, Hai Lu, Jingang Lai and Payman Dehghanian, "Smart Households’ Aggregated Capacity Forecasting for Load Aggregators Under Incentive-Based Demand Response Programs," IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, vol. 56, no. 2, 2020.
[2] Zhaofang Song, Jing Shi, Shujian Li, Zexu Chen, Wangwang Yang and Zitong Zhang, "Day ahead bidding of a load aggregator considering residential consumers demand response uncertainty modeling," Applied Sciences, vol. 10, no. 20, p. 7310, 2020.
[3] Masood Parvania, Mahmud Fotuhi-Firuzabad and Mohammad Shahidehpour, "Optimal Demand Response Aggregation in Wholesale Electricity Markets," IEEE TRANSACTIONS ON SMART GRID, vol. 4, no. 4, 2013.
[4] Homa Rashidizadeh-Kermani, Mostafa Vahedipour-Dahraie, Miadreza Shafie-khah and João P.S. Catalão, "Stochastic programming model for scheduling demand response aggregators considering uncertain market prices and demands," International Journal of Electrical Power & Energy Systems, vol. 113, pp. 528-538, 2019.
[5] "The use of intelligent aggregator agents for advanced control of demand response," WIREs Energy and Environment, vol. 7, no. 3, p. https://doi.org/10.1002/wene.287, 2018.
[6] Xiaoxing Lu, Xinxin Ge, Kangping Li, Fei Wang, Hongtao Shen, Peng Tao, Junjie Hu, Jingang Lai, Zhao Zhen, Miadreza Shafie-khah and João P. S. Catalão, "Optimal Bidding Strategy of Demand Response Aggregator Based On Customers’ Responsiveness Behaviors Modeling Under Different Incentives," IEEE Transactions on Industry Applications, vol. 57, no. 4, pp. 3329-3340, 2021.
[7] Chaman Lal Dewangan, Vineeth Vijayan, Devesh Shukla, S. Chakrabarti, S.N. Singh, Ankush Sharma and Md. Alamgir Hossain, "An improved decentralized scheme for incentive-based demand response from residential customers," Energy, vol. 284, no. Article 128568, 2023.
[8] Valentín Rigoni, Damian Flynn and Andrew Keane, "Coordinating Demand Response Aggregation With LV Network Operational Constraints," IEEE Transactions on Power Systems, vol. 36, no. 2, pp. 979-990, 2021. [9] Lokesh Kumar Panwar, Srikanth Reddy Konda, Ashu Verma, Bijaya K. Panigrahi and Rajesh Kumar, "Demand response aggregator coordinated two‐stage responsive load scheduling in distribution system considering customer behavior," IET Generation, Transmission & Distribution, vol. 11, no. 4, pp. 1023-1032, 2017.
[10] Ehsan Reihani, Mahdi Motalleb, Matsu Thornton and Reza Ghorbani, "A novel approach using flexible scheduling and aggregation to optimize demand response in the developing interactive grid market architecture," Applied Energy, vol. 183, pp. 445-455, 2016.
[11] Wenjie Lv, Jian Wu, Zhao Luo, Min Ding, Xiang Jiang, Hejian Li and Qian Wang, "Load Aggregator-Based Integrated Demand Response for Residential Smart Energy Hubs," Mathematical Problems in Engineering, vol. 1 (2019), no. Article 6925980, p. https://doi.org/10.1155/2019/6925980, 2019.
[12] Biao Xiang, Kangping Li, Xinxin Ge, Fei Wang, Jingang Lai and Payman Dehghanian, "Smart Households' Available Aggregated Capacity Day-ahead Forecast Model for Load Aggregators under Incentive-based Demand Response Program," in 2019 IEEE Industry Applications Society Annual Meeting, Baltimore, MD, USA, 2019.
[13] Michael David de Souza Dutra and Natalia Alguacil, "Optimal residential users coordination via demand response: An exact distributed framework," Applied Energy, vol. 279, no. Article 115851, 2020. [14] Amit Shewale, Anil Mokhade, Nitesh Funde and Neeraj Dhanraj Bokde, "An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem," Energies, vol. 13, no. 16, p. 4266, 2020.
[15] Robin Roche, Siddharth Suryanarayanan, Timothy M. Hansen, Sila Kiliccote and Abdellatif Miraoui, "A multi-agent model and strategy for residential demand response coordination," in 2015 IEEE Eindhoven PowerTech, Eindhoven, Netherlands, 2015.
[16] Zhenyuan Zhang, Zihan Chen and Wei-Jen Lee, "Soft Actor–Critic Algorithm Featured Residential Demand Response Strategic Bidding for Load Aggregators," IEEE Transactions on Industry Application, vol. 58, no. 4, pp. 4298-4308, 2022.
[17] Morteza Vahid-Ghavidel, Mohammad S. Javadi, Sergio F. Santos, Matthew Gough, Behnam Mohammadi-Ivatloo, Miadreza Shafie-Khah and João P. S. Catalão, "Novel Hybrid Stochastic-Robust Optimal Trading Strategy for a Demand Response Aggregator in the Wholesale Electricity Market," IEEE Transactions on Industry Applications, vol. 57, no. 5, pp. 5488-5498, 2021.
[18] Alejandro Fraija, Kodjo Agbossou, Nilson Henao and Sousso Kelouwani, "Peak-to-average ratio analysis of a load aggregator for incentive-based demand response," in IEEE 29th International Symposium on Industrial Electronics, Delft, Netherlands, 2020.
[19] Fadi Elghitani and Weihua Zhuang, "Aggregating a Large Number of Residential Appliances for Demand Response Applications," IEEE Transactions on Smart Grid, vol. 9, no. 5, pp. 5092-5100, 2018.
[20] Pradnya Jadhav, Dagadu More and Surender Reddy Salkuti, "Smart residential distribution energy management system with integration of demand response and Aggregator," Cleaner and Responsible Consumption, vol. 9, p. 100115, 2023.
[21] Hessam Golmohamadi, Reza Keypour, Birgitte Bak-Jensen and Jayakrishnan R. Pillai, "A multi-agent based optimization of residential and industrial demand response aggregators," International Journal of Electrical Power & Energy Systems, vol. 107, pp. 472-485, 2019.
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